2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM)最新文献_第3页

A survey on the challenges of data integration 关于数据集成挑战的调查

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966419

M. Abassi, M. Amnai, A. Choukri, Y. Fakhri, Noreddine Gherabi

引用次数: 1

Prediction of suicidal ideation in a new Arabic annotated dataset 在一个新的阿拉伯语注释数据集中预测自杀意念

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966481

Oumaima Benlaaraj, Ilyas El Jaafari, Ayoub Ellahyani, I. Boutaayamou

引用次数: 1

The Blackhole Attack on Vehicular Network 汽车网络的黑洞攻击

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966449

Achraf Lachheb, E. M. Souidi

引用次数: 1

A Federated Learning Approach for Water Distribution Networks Monitoring 一种用于配水网络监测的联邦学习方法

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966455

Jamal Elhachmi, A. Kobbane

{"title":"A Federated Learning Approach for Water Distribution Networks Monitoring","authors":"Jamal Elhachmi, A. Kobbane","doi":"10.1109/WINCOM55661.2022.9966455","DOIUrl":"https://doi.org/10.1109/WINCOM55661.2022.9966455","url":null,"abstract":"Nowadays, people's demand for water is growing as well as in public, commercial, and industrial sectors. However, the limited character for water resources presents a crucial obstacle to satisfying needs for continued human development. The control and provision of potable water are therefore the most vital challenges for the water supply system. It must use water resources in an efficient manner, and fulfill both quality and quantity demands. For this purpose, the current water supply system uses smart infrastructure that gathers, processes, stores, and delivers water from water sources to users. It is done in a very complex environment with ever-increasing demand, and often conflicting services to deliver. In this work, we developed a Federated Learning (FL) model, which is come in the form of a machine learning setting where clients collaboratively train under the orchestration of a central server while keeping the training data decentralized. The FL embodies the principles of focused data collection and minimization, it can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Implementing on the central server, the FL will iterate on the gateway's learning models using the federated averaging algorithm. Our approach's capacity is shown by addressing reel instances in a real-world issue in which the proposed method finds much better results and a significant improvement in performance compared to the standard approaches while achieving our goal and suggesting a new interesting direction for research.","PeriodicalId":128342,"journal":{"name":"2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM)","volume":"747 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116997109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental assessment of electromagnetic field exposure from 802.11ax devices 802.11ax设备电磁场暴露的实验评估

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966477

Ch. Chountala, J. Chareau, G. Baldini, Fausto Bonavitacola

引用次数: 1

Gastrointestinal diseases classification based on deep learning and transfer learning mechanism 基于深度学习和迁移学习机制的胃肠道疾病分类

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966474

Yassine Oukdach, Zakaria Kerkaou, Mohamed El Ansari, L. Koutti, Ahmed Fouad El Ouafdi

{"title":"Gastrointestinal diseases classification based on deep learning and transfer learning mechanism","authors":"Yassine Oukdach, Zakaria Kerkaou, Mohamed El Ansari, L. Koutti, Ahmed Fouad El Ouafdi","doi":"10.1109/WINCOM55661.2022.9966474","DOIUrl":"https://doi.org/10.1109/WINCOM55661.2022.9966474","url":null,"abstract":"Wireless capsule endoscopy (WCE) is a non-surgical diagnostic procedure enabling the examination of the whole human gastrointestinal tract. Thus, a patient swallows a capsule that travels down the human digestive system and a camera captures wirelessly thousands of images that are transmitted to an external recording device. The diagnosis of these images need a specialist who can identify gastrointestinal abnormalities and it is very time-consuming. Recently, artificial intelligence and deep learning techniques aim to automate disease diagnosis and identi-fication of tumors in the gastrointestinal tract (GI) such as polyps, ulcers and bleeding, etc. In this paper, a deep learning method is proposed for gastrointestinal disease classification. The pre-trained model ResNetSO is fine-tuned through transfer learning to extract deep features from WCE images. The proposed algorithm is trained and tested on the publicly available dataset k-vasir capsule, which contains 14 different classes of gastrointestinal anomalies.","PeriodicalId":128342,"journal":{"name":"2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM)","volume":"69 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132531828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Atmospheric Visibility Image-Based System for Instrument Meteorological Conditions Estimation: A Deep Learning Approach 基于大气能见度图像的仪器气象条件估计系统:一种深度学习方法

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966454

Taha Bouhsine, S. Idbraim, N. Bouaynaya, Husam Alfergani, Kabira Ait Ouadil, C. Johnson

{"title":"Atmospheric Visibility Image-Based System for Instrument Meteorological Conditions Estimation: A Deep Learning Approach","authors":"Taha Bouhsine, S. Idbraim, N. Bouaynaya, Husam Alfergani, Kabira Ait Ouadil, C. Johnson","doi":"10.1109/WINCOM55661.2022.9966454","DOIUrl":"https://doi.org/10.1109/WINCOM55661.2022.9966454","url":null,"abstract":"Visibility degradation is the cause of many road accidents and air crashes around the globe, it is caused and controlled by multiple key factors that hinder the observer from having a clear vision of what's ahead, playing a crucial role in aviation safety. Alas, researchers and engineers created many tools to measure or restore images suffering from those degradations. This paper compares different image-based deep learning architectures for Atmospheric Visibility Range Classification. A Vision Transformer trained from scratch and three pre-trained Convolution Neural Network (CNN) models were examined and contrasted in terms of accuracy, obtaining a validation accuracy of more than 95 %. Our experiment shows that the different models trained on the Federal Aviation Administration (FAA) initial dataset can be used towards building an efficient tool to aid in Flight Control for long-range visibility estimation. Results showed that DenseNet121 was the best performing model with 99 % training accuracy and 98 % validation accuracy and shows early convergence, while the Vision Transformer kept on showing signs of improving but falling behind only by a bit behind DenseNet121 by the end of the experiment.","PeriodicalId":128342,"journal":{"name":"2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127853494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Age of Information and Latency Analysis in Two-Tier Linear IoT Networks 信息时代和两层线性物联网网络的延迟分析

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966470

Imane Cheikh, Rachid Aouami, Bassma Jioudi, Essaid Sabir, M. Sadik, S. Roy

引用次数: 0

Compact Metamaterial Antenna for WLAN Applications 用于WLAN应用的紧凑型超材料天线

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966421

Abdel-Ali Laabadli, Youssef Mejdoub, Abdelkebir El Amri, Mohamed Tarbouch

引用次数: 1

TTIDS : A Time-Driven Trust Based Intrusion Detection System for IoT Networks TTIDS:物联网网络中基于时间驱动信任的入侵检测系统

2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2022-10-26 DOI: 10.1109/WINCOM55661.2022.9966466

Mouad Choukhairi, Y. Fakhri, M. Amnai

{"title":"TTIDS : A Time-Driven Trust Based Intrusion Detection System for IoT Networks","authors":"Mouad Choukhairi, Y. Fakhri, M. Amnai","doi":"10.1109/WINCOM55661.2022.9966466","DOIUrl":"https://doi.org/10.1109/WINCOM55661.2022.9966466","url":null,"abstract":"Consumers are increasingly adopting multiple smart “things” connected via the Internet, such as home appliances, vehicles, and mobile medical equipment, due to the rapid growth of the Internet of Things (IoT). Many of these connections are made possible by IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), a distance vector routing protocol used in IoT networks to build a route to send packets. The diverse nature, as well as the limitations and constraints (Low energy, memory, and storage) of these objects, drive the development of techniques to ensure their safety from malicious attacks. As a result, various approaches to protect the IoT have been designed and developed, however due to the limitations of these methods, they are still insufficient. In this paper an Intrusion Detection System (IDS), based on trust mechanism, was proposed for detecting topology attacks in this sort of environment. The number of identified nodes, energy consumption, memory utilization and the detection time and rate of the IDS were all taken into consideration when evaluating it. We chose Cooja software as the simulator to accomplish this.","PeriodicalId":128342,"journal":{"name":"2022 9th International Conference on Wireless Networks and Mobile Communications (WINCOM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124253104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1